CN111240212A - Tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction - Google Patents

Tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction Download PDF

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CN111240212A
CN111240212A CN202010216657.7A CN202010216657A CN111240212A CN 111240212 A CN111240212 A CN 111240212A CN 202010216657 A CN202010216657 A CN 202010216657A CN 111240212 A CN111240212 A CN 111240212A
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pseudo
solution
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coefficient
actuator
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CN111240212B (en
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赵江
唐旭阳
蔡志浩
王英勋
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Beihang University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G05D1/0816—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
    • G05D1/0825—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability using mathematical models
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G05D1/0816—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
    • G05D1/0841—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability to prevent a coupling between different modes
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft

Abstract

The invention belongs to the field of unmanned aerial vehicle control distribution, and relates to a tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction. The optimal predictive control allocation method introduces a null space concept on the basis of the traditional pseudo-inverse method, and expands the reachable control set of the pseudo-inverse solution through the compensation term of the null space vector, so that the reachable set of the torque of the traditional pseudo-inverse method is expanded, and the control allocation efficiency is improved. Aiming at the phenomena of control redundancy and complex disturbance existing in the transitional mode of the tilt rotor unmanned aerial vehicle, the optimal predictive control distribution method can realize complete distribution of torque in a reachable set, realize control decoupling, ensure stable attitude in the transitional process and improve the control effect.

Description

Tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction
Technical Field
The invention belongs to the field of unmanned aerial vehicle control distribution, and relates to a tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction.
Background
Rotor unmanned aerial vehicle verts combines the advantage of traditional helicopter and fixed wing aircraft, can realize VTOL, and high-speed cruise is applicable to different actual scenes, satisfies different task demands. But the tiltrotor design places even greater demands on control distribution. Taking the transition stage as an example, regarding the attitude control of the tilt rotor unmanned aerial vehicle, the rotor and the control surface can both play a control role, and the phenomenon of control redundancy exists, and the forward-leaning rotor has component force in the forward direction and the vertical direction, so that the control coupling phenomenon exists in the roll control and the yaw control of the rotor. In addition, due to the fact that a complex internal and external disturbance phenomenon exists in the transition stage, the posture fluctuation is large in the transition process, the safety of the unmanned aerial vehicle is threatened, the control redundancy and control coupling problems need to be solved by applying a control distribution technology, meanwhile, the maneuvering capacity of the tilt rotor unmanned aerial vehicle can be improved by completely distributing the expected torque, and the stability and the safety in the transition process are guaranteed.
Control allocation techniques are divided into two categories in principle: linear allocation algorithms and non-linear allocation algorithms. The nonlinear algorithm is more suitable for the problems of high dimensionality and strong nonlinearity, but is difficult to be practically applied due to the limitation of airborne capacity. The linear distribution algorithm is simple to implement and is widely applied in engineering practice, but the method cannot realize complete distribution in a reachable set of torque.
Disclosure of Invention
Aiming at the problem that the linear distribution algorithm cannot realize complete distribution in the reachable set of the torque, the invention provides the tilt rotor unmanned aerial vehicle control distribution method based on the optimization prediction.
The invention provides a tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction, which comprises the following steps:
s1: calculating a control efficiency matrix B of the tilt rotor unmanned aerial vehicle in the current mode;
s2: inputting unitized vector of expected triaxial moment
Figure BDA0002424651470000021
Is the desired triaxial moment vector, where L, M, N represents roll, pitch and yaw moments, respectively;
s3: computing pseudo-inverse solutions
Figure BDA0002424651470000022
And solving the pseudo-inverse vector
Figure BDA0002424651470000023
The unit pseudo inverse solution is obtained by the unit processing
Figure BDA0002424651470000024
S4: based on the unit pseudo-inverse solution calculated in step S3
Figure BDA0002424651470000025
Judging a first saturated actuator;
s5, calculating a pseudo-inverse solution coefficient and a zero space coefficient;
s6: determining a next saturated actuator and updating the optimized prediction solution coefficient;
s7: judging whether all the actuators are traversed, if the optimal prediction solution coefficients of all the actuators in the saturation process are determined, performing the step S8, otherwise, continuously executing the step S6 until all the actuators are saturated;
s8: the look-up table determines an optimal predicted solution at any desired torque.
Further, the step S1 specifically includes the following steps:
and calculating a control efficiency matrix in the transition process of the tilt rotor unmanned aerial vehicle. Confirm a certain state point in the rotor unmanned aerial vehicle that verts transition corridor, obtain safe and reliable's airspeed V and nacelle angle of verting delta, carry out balancing and linearization to rotor unmanned aerial vehicle that verts six degrees of freedom nonlinear dynamical models under current state, obtain the perturbation equation of state:
Figure BDA0002424651470000026
wherein x is a system state variable; y is the system output; u is the system control input; superscript · denotes first derivative; A. b isuAnd C both represent constant matrices;
extracting coefficient matrix BuThe moment coefficient in obtains control efficiency matrix B under the current mode of the tilt rotor unmanned aerial vehicle:
Figure BDA0002424651470000031
wherein, CL,CM,CNRespectively representing a rolling moment coefficient, a pitching moment coefficient and a yawing moment coefficient; { Delta ]gAnd | g |, 1,2, … m } represents the actuators, and m is the number of actuators.
Further, the step S3 specifically includes the following steps:
solving a control distribution problem
Figure BDA0002424651470000032
By solving the inverse B of the control efficiency matrix B-1To obtain a pseudo-inverse solution
Figure BDA0002424651470000033
To pseudo inverse solution
Figure BDA0002424651470000034
Performing unitization to obtain unit pseudo inverse solution
Figure BDA0002424651470000035
Further, the step S4 specifically includes the following steps:
unit pseudo-inverse solution calculated based on step S4
Figure BDA0002424651470000036
Finding a pseudo-inverse solution system that saturates only one actuatorNumber a0So that
Figure BDA0002424651470000037
Satisfies formula (2):
Figure BDA0002424651470000038
in the formula (I), the compound is shown in the specification,
Figure BDA0002424651470000039
representing the maximum moment along the desired moment direction that the pseudo-inverse can output within the achievable control set Ω;
Figure BDA00024246514700000310
representing a pseudo-inverse solution portion of the optimized predictive solution;
Figure BDA00024246514700000311
representing a null-space solution portion of the optimized prediction solution; omega is the set of reachable controls,
Figure BDA00024246514700000312
wherein the content of the first and second substances,
Figure BDA00024246514700000313
representing actuator position vector, uiIndicating the position of the actuator i, m is the number of actuators,
Figure BDA00024246514700000314
representing an m-dimensional vector space, ulwriRepresents the lower limit, u, of the position of the actuator iupriRepresents the upper limit of the position of the actuator i; δ (Ω) represents the boundary of the reachable control set; pSminA set of pseudo-inverse solutions is represented,
Figure BDA00024246514700000315
phi is the achievable set of torques,
Figure BDA00024246514700000316
Figure BDA00024246514700000317
representing an n-dimensional vector space of the image,
Figure BDA00024246514700000318
representing a torque vector within the achievable set phi of torques; calculating pseudo-inverse solution coefficients a that saturate only one actuator0And obtaining a first saturated actuator.
Further, the step S5 specifically includes the following steps:
the pseudo-inverse solution coefficient a obtained in step S4 when only one actuator is saturated0Corresponding zero space coefficient
Figure BDA0002424651470000041
Let the pseudo-inverse solution coefficient at the next actuator saturation be a1Solving the corresponding zero space coefficient
Figure BDA0002424651470000042
Setting a locally optimized cost function
Figure BDA0002424651470000043
As shown in formula (3):
Figure BDA0002424651470000044
wherein, superscript T represents a transpose matrix;
Figure BDA0002424651470000045
representing a lagrange operator;
Figure BDA0002424651470000046
an orthonormal basis representing a null space;
Figure BDA0002424651470000047
representing the pseudo-inverse solution coefficient as a0Pseudo-inverse solution of time correspondence
Figure BDA0002424651470000048
Figure BDA0002424651470000049
Representing the pseudo-inverse solution coefficient as a1Pseudo-inverse solution of time correspondence
Figure BDA00024246514700000410
The subscript sat represents the element in the vector corresponding to the saturation actuator;
obtaining a null-space solution with minimum two norms by solving the formula (3)
Figure BDA00024246514700000411
Figure BDA00024246514700000412
Wherein the content of the first and second substances,
Figure BDA00024246514700000413
further, the step S6 specifically includes the following steps:
assuming that the actuator gradually reaches full saturation from the unsaturated state as the expected torque amplitude gradually increases, the saturation order and the pseudo-inverse solution coefficient corresponding to the saturation set need to be determined for this purpose, and the pseudo-inverse solution coefficient a of the next actuator at saturation in step S5 is solved1；
Defining:
Figure BDA00024246514700000414
wherein the content of the first and second substances,
Figure BDA00024246514700000415
indicating an input desired torque of
Figure BDA00024246514700000416
Pseudo-inverse solution of time;
Figure BDA00024246514700000417
indicating an input desired torque of
Figure BDA00024246514700000418
Pseudo-inverse solution of time; Δ a represents the pseudo-inverse coefficient increment;
solving the pseudo-inverse solution of equation (4)
Figure BDA00024246514700000419
Respectively corresponding null-space solutions
Figure BDA00024246514700000420
The optimal control quantity is obtained as follows:
Figure BDA0002424651470000051
Figure BDA0002424651470000052
definition of units per a0Rate modification of optimal control solution under change
Figure BDA0002424651470000053
Figure BDA0002424651470000054
Wherein the content of the first and second substances,
Figure BDA0002424651470000055
to represent
Figure BDA0002424651470000056
The value corresponding to the medium unsaturated actuator i;
Figure BDA0002424651470000057
to represent
Figure BDA0002424651470000058
The value corresponding to the medium unsaturated actuator i; s1Representing a set of unsaturated actuators;
with gainChanging the trend of each unsaturated actuator
Figure BDA0002424651470000059
Comprises the following steps:
Figure BDA00024246514700000510
wherein sgn represents a sign function;
obtaining an actuator saturation evaluation parameter delta a from equations (6) and (7)0(i)：
Figure BDA00024246514700000511
Evaluating the parameter deltaa for minimal actuator saturation0(imin) Corresponding index iminAdding a set S of currently saturated actuators2Calculating the pseudo-inverse solution coefficient a when the next actuator is saturated1：
a1＝a0+Δa+δa0(imin) (9)
Calculating a pseudo inverse solution coefficient a by the formula (4)1Corresponding zero space coefficient
Figure BDA00024246514700000512
Obtaining the current saturation set S2Optimized predictive solution of
Figure BDA00024246514700000513
Figure BDA00024246514700000514
Further, the step S8 specifically includes the following steps:
is provided with
Figure BDA0002424651470000061
Represents the optimized prediction solution coefficient when the iteration number is s, s is 0,1, and k is 1, and the two-norm of the corresponding vector is
Figure BDA0002424651470000062
Will be provided with
Figure BDA0002424651470000063
The set is written in matrix form:
Figure BDA0002424651470000064
wherein k represents the iteration number when the actuators are all saturated;
will be two norms
Figure BDA0002424651470000065
The set is written in matrix form:
Figure BDA0002424651470000066
for an arbitrary moment vector
Figure BDA0002424651470000067
And is
Figure BDA0002424651470000068
Obtaining an optimized predictive solution by equations (11) and (12)
Figure BDA0002424651470000069
The following three cases are specifically discussed:
1)
Figure BDA00024246514700000610
2)
Figure BDA00024246514700000611
3)
Figure BDA00024246514700000612
1) the situation is as follows: at this time, the pseudo-inverse method is used to satisfy the position clipping requirement, so that:
Figure BDA00024246514700000613
2) the situation is as follows: because the desired torque exceeds the torque reachable set Φ, the optimal prediction solution is located at the boundary δ (Ω) of the reachable control set, i.e., the optimal prediction solution coefficients
Figure BDA00024246514700000614
Thus:
Figure BDA00024246514700000615
3) the situation is as follows: firstly, determining s by means of piecewise linearization:
Figure BDA00024246514700000616
setting the equation:
Figure BDA0002424651470000071
where G is the optimal prediction solution coefficient determined by the indices i and i +1, and C is written as:
Figure BDA0002424651470000072
wherein the up and down vectors of C are the regression intercept and the slope gain vector, respectively, therefore
Figure BDA0002424651470000074
Comprises the following steps:
Figure BDA0002424651470000073
the invention has the beneficial effects that:
the invention can realize the optimization of the boundary of the reachable set of the torque, thereby improving the distribution efficiency of the traditional pseudo-inverse method, compared with the mode of optimizing in the whole reachable set of the control by setting a target optimization function in the common optimization method, the invention improves the calculation speed by optimizing along the direction of the expected torque vector, lightens the calculation burden and has the capability of on-line optimization.
Drawings
Fig. 1 is a flowchart of an optimal prediction based tilt rotor unmanned aerial vehicle control distribution method of the present invention;
FIG. 2 is a schematic diagram of an optimization prediction algorithm;
FIG. 3 is a schematic view of the actuator according to an embodiment of the present invention showing a saturation tendency;
FIG. 4 is a comparison graph of the output torque of the optimized prediction algorithm of the present invention and the conventional pseudo-inverse method.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples, it being understood that the examples described below are intended to facilitate the understanding of the invention, and are not intended to limit it in any way.
As shown in fig. 1, the tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction of the invention comprises the following steps:
s1: calculating a control efficiency matrix B under a transition mode of the tilt rotor unmanned aerial vehicle;
selecting a nacelle angle delta of 60 degrees and a speed V of 16m/s under the transition mode of the tilt rotor unmanned aerial vehicle, balancing and linearizing a six-degree-of-freedom nonlinear dynamics model of the tilt rotor unmanned aerial vehicle under the current state, and obtaining a coefficient matrix B by a small disturbance equationuThe extraction control performance matrix B is:
Figure BDA0002424651470000081
the actuator position amplitude limit is:
umax＝[2.5 2.5 10 10 15 15 25 25 15 15]T
umin＝[-2.5 -2.5 -10 -10 -15 -15 -25 -25 -15 -15]T
wherein u ismaxIs at the position of an actuatorLimit uminIs the lower limit of the actuator position.
Normalizing the position amplitude limit of the actuator:
Figure BDA0002424651470000082
w represents a coefficient matrix to obtain
Figure BDA0002424651470000083
And
Figure BDA0002424651470000084
Figure BDA0002424651470000085
Figure BDA0002424651470000086
Figure BDA0002424651470000087
wherein the content of the first and second substances,
Figure BDA0002424651470000088
representing a normalized control effectiveness matrix;
Figure BDA0002424651470000089
representing a normalized upper actuator position limit;
Figure BDA00024246514700000810
representing a normalized actuator position lower limit;
s2: inputting desired unit triaxial moment vector
Figure BDA00024246514700000811
Setting an input desired triaxial moment vector
Figure BDA00024246514700000812
To proceed it withIs processed into a unit to obtain
Figure BDA00024246514700000813
Figure BDA00024246514700000814
S3: computing pseudo-inverse solutions
Figure BDA00024246514700000815
And solve the pseudo-inverse
Figure BDA00024246514700000816
The unit pseudo inverse solution is obtained by the unit processing
Figure BDA00024246514700000817
Calculating to obtain pseudo inverse solution by pseudo inverse method
Figure BDA00024246514700000818
Figure BDA00024246514700000819
To pseudo inverse solution
Figure BDA00024246514700000820
The unit pseudo inverse solution is obtained by the unit processing
Figure BDA00024246514700000821
Figure BDA0002424651470000091
S4: actuator for judging first saturation
Setting:
Figure BDA0002424651470000092
wherein u islimAn upper or lower actuator position limit (by)
Figure BDA0002424651470000093
Symbol judgment of) 0.1080 is the largest element in r, and the corresponding actuator 1 is the actuator most easily saturated in position, that is, as the amplitude of the input expected torque vector increases, the actuator 1 reaches saturation first;
s5: calculating the pseudo-inverse solution coefficient aiAnd zero space coefficient bi
As shown in fig. 3, the optimal prediction solution calculated by the optimal prediction method includes two parts: the method comprises a pseudo-inverse solution and a null-space solution, wherein the pseudo-inverse solution realizes the expected torque output, and the null-space solution limits the part of the pseudo-inverse solution exceeding the reachable control set without changing the torque output, so that the reachable torque set is expanded.
The actuator j that first reaches saturation is obtained in step S4 { j ∈ 1: m | r (j) | | r | | |∞} (j is the index corresponding to the actuator, m is the number of the actuators, | r | | calculation∞The largest value of r), the pseudo-inverse solution coefficient at which the pseudo-inverse solution reaches the boundary is calculated
Figure BDA0002424651470000094
Coefficient of null space
Figure BDA0002424651470000095
S6: determining a next saturated actuator and optimizing the updating of the prediction solution coefficient;
calculating the corresponding pseudo-inverse solution coefficient a when the next actuator is saturated1；
Defining:
Figure BDA0002424651470000096
wherein the content of the first and second substances,
Figure BDA0002424651470000097
indicating an input desired torque of
Figure BDA0002424651470000098
The pseudo-inverse solution of the time is,
Figure BDA0002424651470000099
indicating an input desired torque of
Figure BDA00024246514700000910
Pseudo-inverse solution of time. Is provided with
Figure BDA00024246514700000911
The corresponding is obtained from formula (4)
Figure BDA00024246514700000912
The optimal control quantity is as follows:
Figure BDA0002424651470000101
Figure BDA0002424651470000102
definition of units per a0The rate of the optimal control solution under change is changed to:
Figure BDA0002424651470000103
wherein the content of the first and second substances,
Figure BDA0002424651470000104
to represent
Figure BDA0002424651470000105
The value corresponding to the medium unsaturated actuator i;
Figure BDA0002424651470000106
to represent
Figure BDA0002424651470000107
The value corresponding to the medium unsaturated actuator i; s1Representing a set of unsaturated actuators;
Figure BDA0002424651470000108
the trend of the variation of the respective unsaturated actuator (towards a maximum or minimum value) as the gain varies is described:
Figure BDA0002424651470000109
wherein sgn represents a sign function;
Figure BDA00024246514700001010
represents the upper limit of the position of the actuator i;
Figure BDA00024246514700001011
represents the lower limit of the position of the actuator i;
obtaining delta a from the two formulas (6) and (7)0i：
Figure BDA00024246514700001012
Will be the smallest delta a0(i) Corresponding index imin(imin2) adding a saturated actuator set S2Calculating the corresponding pseudo-inverse solution coefficient a1：
a1＝a0+Δa+δa0(i)＝1.5782
The zero space coefficient is obtained by the calculation of the formula (4)
Figure BDA00024246514700001013
Obtaining the current saturation set S2Optimized predictive solution of
Figure BDA00024246514700001014
Figure BDA0002424651470000111
Figure BDA0002424651470000112
Figure BDA0002424651470000113
S7: judging whether to traverse all the actuators
Judging whether all the actuators are traversed or not, if the optimal prediction solution coefficients of all the actuators in the saturation process are determined, performing the step S8, otherwise, continuously executing the step S6 until all the actuators are saturated, and obtaining the saturation sequence of the actuators as shown in FIG. 3;
s8: lookup table method for determining control gain under any expected torque
Is provided with
Figure BDA0002424651470000114
Represents the optimized prediction solution coefficient when the iteration number is s, and the two norms of the corresponding vector are
Figure BDA0002424651470000115
Will be provided with
Figure BDA0002424651470000116
The set is written in matrix form:
Figure BDA0002424651470000117
wherein k represents the iteration number when the actuators are all saturated;
will be two norms
Figure BDA0002424651470000118
The set is written in matrix form:
Figure BDA0002424651470000119
for an arbitrary moment vector
Figure BDA00024246514700001110
And is
Figure BDA00024246514700001111
Obtaining an optimized predictive solution by equations (11) and (12)
Figure BDA00024246514700001112
The following three cases are specifically discussed:
1)
Figure BDA00024246514700001113
2)
Figure BDA00024246514700001114
3)
Figure BDA00024246514700001115
1) the situation is as follows: at this time, the pseudo-inverse method can meet the position amplitude limiting requirement, so that:
Figure BDA0002424651470000121
2) the situation is as follows: since the desired torque exceeds the torque reachable set Φ, the optimal prediction method solution lies at the boundary δ (Ω) of the reachable control set, i.e. the optimal prediction solution coefficients
Figure BDA0002424651470000122
Thus:
Figure BDA0002424651470000123
3) the situation is as follows: firstly, determining s by means of piecewise linearization:
Figure BDA0002424651470000124
setting the equation:
Figure BDA0002424651470000125
where G is the optimal prediction solution coefficient determined by the indices i and i +1, and C is written as:
Figure BDA0002424651470000126
wherein, the upper and lower line vectors of C are regression intercept and slope gain vector respectively,
Figure BDA0002424651470000127
comprises the following steps:
Figure BDA0002424651470000128
the incoming data yields:
Figure BDA0002424651470000129
torque achievable output torque pair ratio in the set is shown in fig. 4;
output torque of an optimized prediction algorithm:
Figure BDA00024246514700001210
output torque by pseudo-inverse method:
Figure BDA0002424651470000131
it will be apparent to those skilled in the art that various modifications and improvements can be made to the embodiments of the present invention without departing from the inventive concept thereof, and these modifications and improvements are intended to be within the scope of the invention.

Claims (7)

1. A tilt rotor unmanned aerial vehicle control distribution method based on optimization prediction is characterized by comprising the following steps:
s1: calculating a control efficiency matrix B of the tilt rotor unmanned aerial vehicle in the current mode;
s2: inputting unitized vector of expected triaxial moment
Figure FDA0002424651460000011
Figure FDA0002424651460000012
Is the desired triaxial moment vector, where L, M, N represents roll, pitch and yaw moments, respectively;
s3: computing pseudo-inverse solutions
Figure FDA0002424651460000013
And solving the pseudo-inverse vector
Figure FDA0002424651460000014
The unit pseudo inverse solution is obtained by the unit processing
Figure FDA0002424651460000015
S4: based on the unit pseudo-inverse solution calculated in step S3
Figure FDA0002424651460000016
Judging a first saturated actuator;
s5, calculating a pseudo-inverse solution coefficient and a zero space coefficient;
s6: determining a next saturated actuator and updating the optimized prediction solution coefficient;
s7: judging whether all the actuators are traversed, if the optimal prediction solution coefficients of all the actuators in the saturation process are determined, performing the step S8, otherwise, continuously executing the step S6 until all the actuators are saturated;
s8: the look-up table determines an optimal predicted solution at any desired torque.
2. The method according to claim 1, wherein step S1 is implemented as follows:
calculate the control efficiency matrix in the rotor unmanned aerial vehicle that verts transition process, confirm a certain state point in the rotor unmanned aerial vehicle that verts transition corridor, obtain safe and reliable's flying speed V and nacelle angle of verting delta, carry out balancing and linearization to the rotor unmanned aerial vehicle that verts six degrees of freedom nonlinear dynamical model under current state, obtain the perturbation equation of state:
Figure FDA0002424651460000017
wherein x is a system state variable; y is the system output; u is the system control input; superscript · denotes first derivative; A. b isuAnd C both represent constant matrices;
extracting coefficient matrix BuThe moment coefficient in obtains control efficiency matrix B under the current mode of the tilt rotor unmanned aerial vehicle:
Figure FDA0002424651460000021
wherein, CL,CM,CNRespectively representing a rolling moment coefficient, a pitching moment coefficient and a yawing moment coefficient; { Delta ]gAnd | g |, 1,2, … m } represents the actuators, and m is the number of actuators.
3. The method according to claim 1, wherein step S3 is implemented as follows:
solving a control distribution problem
Figure FDA0002424651460000022
By solving the inverse B of the control efficiency matrix B-1To obtain a pseudo-inverse solution
Figure FDA0002424651460000023
To pseudo inverse solution
Figure FDA0002424651460000024
Performing unitization to obtain unit pseudo inverse solution
Figure FDA0002424651460000025
4. The method according to claim 1, wherein step S4 is implemented as follows:
unit pseudo-inverse solution calculated based on step S4
Figure FDA0002424651460000026
Finding the pseudo-inverse solution coefficient a that saturates only one actuator0So that
Figure FDA0002424651460000027
Satisfies formula (2):
Figure FDA0002424651460000028
in the formula (I), the compound is shown in the specification,
Figure FDA0002424651460000029
representing the maximum moment along the desired moment direction that the pseudo-inverse can output within the achievable control set Ω;
Figure FDA00024246514600000210
representing a pseudo-inverse solution portion of the optimized predictive solution;
Figure FDA00024246514600000211
representing a null-space solution portion of the optimized prediction solution; omega is the set of reachable controls,
Figure FDA00024246514600000212
wherein the content of the first and second substances,
Figure FDA00024246514600000213
representing actuator position vector, uiIndicating the position of the actuator i, m is the number of actuators,
Figure FDA00024246514600000214
representing an m-dimensional vector space, ulwriRepresents the lower limit, u, of the position of the actuator iupriRepresents the upper limit of the position of the actuator i; δ (Ω) represents the boundary of the reachable control set; pSminA set of pseudo-inverse solutions is represented,
Figure FDA00024246514600000215
phi is the achievable set of torques,
Figure FDA00024246514600000216
Figure FDA00024246514600000217
representing an n-dimensional vector space of the image,
Figure FDA00024246514600000218
representing a torque vector within the achievable set phi of torques; calculating pseudo-inverse solution coefficients a that saturate only one actuator0And obtaining a first saturated actuator.
5. The method according to claim 1, wherein step S5 is implemented as follows:
the pseudo-inverse solution coefficient a obtained in step S4 when only one actuator is saturated0Corresponding zero space coefficient
Figure FDA0002424651460000031
Let the pseudo-inverse solution coefficient at the next actuator saturation be a1Solving the corresponding zero space coefficient
Figure FDA0002424651460000032
Setting a locally optimized cost function
Figure FDA0002424651460000033
As shown in formula (3):
Figure FDA0002424651460000034
wherein, superscript T represents a transpose matrix;
Figure FDA0002424651460000035
representing a lagrange operator;
Figure FDA0002424651460000036
an orthonormal basis representing a null space;
Figure FDA0002424651460000037
representing the pseudo-inverse solution coefficient as a0Pseudo-inverse solution of time correspondence
Figure FDA0002424651460000038
Figure FDA0002424651460000039
Representing the pseudo-inverse solution coefficient as a1Pseudo-inverse solution of time correspondence
Figure FDA00024246514600000310
The subscript sat represents the element in the vector corresponding to the saturation actuator;
obtaining a null-space solution with minimum two norms by solving the formula (3)
Figure FDA00024246514600000311
Figure FDA00024246514600000312
Wherein the content of the first and second substances,
Figure FDA00024246514600000313
6. the method according to claim 5, wherein step S6 is implemented as follows:
assuming that the actuator gradually reaches full saturation from the unsaturated state as the expected torque amplitude gradually increases, the saturation order and the pseudo-inverse solution coefficient corresponding to the saturation set need to be determined for this purpose, and the pseudo-inverse solution coefficient a of the next actuator at saturation in step S5 is solved1；
Defining:
Figure FDA00024246514600000314
wherein the content of the first and second substances,
Figure FDA00024246514600000315
indicating an input desired torque of
Figure FDA00024246514600000316
Pseudo-inverse solution of time;
Figure FDA00024246514600000317
indicating an input desired torque of
Figure FDA00024246514600000318
Pseudo-inverse solution of time; Δ a represents the pseudo-inverse coefficient increment;
solving the pseudo-inverse solution of equation (4)
Figure FDA00024246514600000319
Respectively corresponding null-space solutions
Figure FDA00024246514600000320
The optimal control quantity is obtained as follows:
Figure FDA0002424651460000041
Figure FDA0002424651460000042
definition of units per a0Rate modification of optimal control solution under change
Figure FDA0002424651460000043
Figure FDA0002424651460000044
Wherein the content of the first and second substances,
Figure FDA0002424651460000045
to represent
Figure FDA0002424651460000046
The value corresponding to the medium unsaturated actuator i;
Figure FDA0002424651460000047
to represent
Figure FDA0002424651460000048
The value corresponding to the medium unsaturated actuator i; s1Representing a set of unsaturated actuators;
trend of variation of each unsaturated actuator with variation of gain
Figure FDA0002424651460000049
Comprises the following steps:
Figure FDA00024246514600000410
wherein sgn represents a sign function;
obtaining an actuator saturation evaluation parameter delta a from equations (6) and (7)0(i)：
Figure FDA00024246514600000411
Evaluating the parameter deltaa for minimal actuator saturation0(imin) Corresponding index iminAdding a set S of currently saturated actuators2Calculating the pseudo-inverse solution coefficient a when the next actuator is saturated1：
a1＝a0+Δa+δa0(imin) (9)
Calculating a pseudo inverse solution coefficient a by the formula (4)1Corresponding zero space coefficient
Figure FDA00024246514600000412
Obtaining the current saturation set S2Optimized predictive solution of
Figure FDA00024246514600000413
Figure FDA00024246514600000414
7. The method according to claim 1, wherein step S8 is implemented as follows:
is provided with
Figure FDA0002424651460000051
Represents the optimized prediction solution coefficient when the iteration number is s, s is 0,1, and k is 1, and the two-norm of the corresponding vector is
Figure FDA0002424651460000052
Will be provided with
Figure FDA0002424651460000053
The set is written in matrix form:
Figure FDA0002424651460000054
wherein k represents the iteration number when the actuators are all saturated;
will be two norms
Figure FDA0002424651460000055
The set is written in matrix form:
Figure FDA0002424651460000056
for an arbitrary moment vector
Figure FDA0002424651460000057
And is
Figure FDA0002424651460000058
Obtaining an optimized predictive solution by equations (11) and (12)
Figure FDA0002424651460000059
The following three cases are specifically discussed:
1)
Figure FDA00024246514600000510
2)
Figure FDA00024246514600000511
3)
Figure FDA00024246514600000512
1) the situation is as follows: at this time, the pseudo-inverse method is used to satisfy the position clipping requirement, so that:
Figure FDA00024246514600000513
2) the situation is as follows: because the desired torque exceeds the torque reachable set Φ, the optimal prediction solution is located at the boundary δ (Ω) of the reachable control set, i.e., the optimal prediction solution coefficients
Figure FDA00024246514600000514
Thus:
Figure FDA00024246514600000515
3) the situation is as follows: firstly, determining s by means of piecewise linearization:
Figure FDA00024246514600000516
setting the equation:
Figure FDA0002424651460000061
where G is the optimal prediction solution coefficient determined by the indices i and i +1, and C is written as:
Figure FDA0002424651460000062
wherein the up and down vectors of C are the regression intercept and the slope gain vector, respectively, therefore
Figure FDA0002424651460000063
Comprises the following steps:
Figure FDA0002424651460000064
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112068582A (en) * 2020-09-24 2020-12-11 北京航空航天大学 Method for identifying transition mode model of tilt rotor unmanned aerial vehicle
CN112198817A (en) * 2020-09-23 2021-01-08 深圳市领峰电动智能科技有限公司 Unmanned aerial vehicle control method, device, equipment, unmanned aerial vehicle and medium
CN112904876A (en) * 2021-01-14 2021-06-04 北京航空航天大学 High-mobility micro unmanned aerial vehicle control distribution method considering motor saturation
CN114489144A (en) * 2022-04-08 2022-05-13 中国科学院自动化研究所 Unmanned aerial vehicle autonomous maneuver decision method and device and unmanned aerial vehicle
DE102021102251A1 (en) 2021-02-01 2022-08-04 Volocopter Gmbh Method and control unit for controlling an overdetermined system, system and aircraft
CN116719332A (en) * 2023-05-22 2023-09-08 四川大学 Unmanned aerial vehicle position and posture regulation and control system and method based on tilting rotation wing

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104035447A (en) * 2014-06-27 2014-09-10 金陵科技学院 Dynamic control reallocation based unmanned aerial vehicle attitude fault-tolerance control method
CN106990790A (en) * 2017-06-06 2017-07-28 成都纵横自动化技术有限公司 A kind of anti-saturation multi-rotor aerocraft control method
US20180074516A1 (en) * 2015-04-16 2018-03-15 Nec Corporation Control apparatus, control method, and storage medium
CN107992080A (en) * 2017-12-25 2018-05-04 成都纵横自动化技术有限公司 control distribution method, device and multi-rotor aerocraft
CN108628167A (en) * 2018-05-08 2018-10-09 南京邮电大学 The fault tolerant control method of flight control system based on self-adjusted block technology
CN109270947A (en) * 2018-12-13 2019-01-25 北京航空航天大学 Tilting rotor wing unmanned aerial vehicle flight control system
CN109976368A (en) * 2019-04-16 2019-07-05 南京航空航天大学 A kind of flying vehicles control distribution method based on direct distribution method and kernel
CN110413000A (en) * 2019-05-28 2019-11-05 北京航空航天大学 A kind of hypersonic aircraft based on deep learning reenters prediction and corrects fault-tolerant method of guidance

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104035447A (en) * 2014-06-27 2014-09-10 金陵科技学院 Dynamic control reallocation based unmanned aerial vehicle attitude fault-tolerance control method
US20180074516A1 (en) * 2015-04-16 2018-03-15 Nec Corporation Control apparatus, control method, and storage medium
CN106990790A (en) * 2017-06-06 2017-07-28 成都纵横自动化技术有限公司 A kind of anti-saturation multi-rotor aerocraft control method
CN107992080A (en) * 2017-12-25 2018-05-04 成都纵横自动化技术有限公司 control distribution method, device and multi-rotor aerocraft
CN108628167A (en) * 2018-05-08 2018-10-09 南京邮电大学 The fault tolerant control method of flight control system based on self-adjusted block technology
CN109270947A (en) * 2018-12-13 2019-01-25 北京航空航天大学 Tilting rotor wing unmanned aerial vehicle flight control system
CN109976368A (en) * 2019-04-16 2019-07-05 南京航空航天大学 A kind of flying vehicles control distribution method based on direct distribution method and kernel
CN110413000A (en) * 2019-05-28 2019-11-05 北京航空航天大学 A kind of hypersonic aircraft based on deep learning reenters prediction and corrects fault-tolerant method of guidance

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112198817A (en) * 2020-09-23 2021-01-08 深圳市领峰电动智能科技有限公司 Unmanned aerial vehicle control method, device, equipment, unmanned aerial vehicle and medium
CN112198817B (en) * 2020-09-23 2022-07-12 峰飞航空科技(昆山)有限公司 Unmanned aerial vehicle control method, device, equipment, unmanned aerial vehicle and medium
CN112068582A (en) * 2020-09-24 2020-12-11 北京航空航天大学 Method for identifying transition mode model of tilt rotor unmanned aerial vehicle
CN112068582B (en) * 2020-09-24 2022-02-18 北京航空航天大学 Method for identifying transition mode model of tilt rotor unmanned aerial vehicle
CN112904876A (en) * 2021-01-14 2021-06-04 北京航空航天大学 High-mobility micro unmanned aerial vehicle control distribution method considering motor saturation
CN112904876B (en) * 2021-01-14 2022-06-21 北京航空航天大学 High-mobility micro unmanned aerial vehicle control distribution method considering motor saturation
DE102021102251A1 (en) 2021-02-01 2022-08-04 Volocopter Gmbh Method and control unit for controlling an overdetermined system, system and aircraft
US20220250766A1 (en) * 2021-02-01 2022-08-11 Volocopter Gmbh Method and control unit for controlling an overdetermined system, system and aircraft
CN114489144A (en) * 2022-04-08 2022-05-13 中国科学院自动化研究所 Unmanned aerial vehicle autonomous maneuver decision method and device and unmanned aerial vehicle
CN116719332A (en) * 2023-05-22 2023-09-08 四川大学 Unmanned aerial vehicle position and posture regulation and control system and method based on tilting rotation wing
CN116719332B (en) * 2023-05-22 2024-01-30 四川大学 Unmanned aerial vehicle position and posture regulation and control system and method based on tilting rotor

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